title: Spikenaut SNN Telemetry Data
emoji: 🧠
license:
- mit
- apache-2.0
task_categories:
- reinforcement-learning
- time-series-forecasting
size_categories:
- 100K<n<1M
configs:
- config_name: gpu_telemetry
data_files:
- split: train
path:
- v2_parquet/gpu_telemetry/train-*.parquet
- config_name: mining
data_files:
- split: train
path:
- v2_parquet/mining/train-*.parquet
- config_name: hft
data_files:
- split: train
path:
- v2_parquet/hft/train-*.parquet
- config_name: qubic_ticks
data_files:
- split: train
path:
- v2_parquet/qubic_ticks/train-*.parquet
- config_name: gpu_telemetry_v3
data_files:
- split: full
path:
- v3/gpu_telemetry/full-*.parquet
- config_name: qubic_signals
data_files:
- split: full
path:
- v3/qubic_signals/full-*.parquet
- config_name: state_telemetry
data_files:
- split: train
path:
- v3/state_telemetry/train-*.parquet
- split: validation
path:
- v3/state_telemetry/validation-*.parquet
- split: test
path:
- v3/state_telemetry/test-*.parquet
- config_name: outcomes
data_files:
- split: train
path:
- v3/outcomes/train-*.parquet
- split: validation
path:
- v3/outcomes/validation-*.parquet
- split: test
path:
- v3/outcomes/test-*.parquet
- config_name: encoding_params
data_files:
- split: train
path:
- v3/encoding_params/train-*.parquet
dataset_info:
- config_name: gpu_telemetry
features:
- name: vddcr_gfx_v
dtype: float64
- name: vram_temp_c
dtype: float64
- name: gpu_temp_c
dtype: float64
- name: power_w
dtype: float64
- name: gpu_clock_mhz
dtype: float64
- name: mem_clock_mhz
dtype: float64
- name: clock_mhz
dtype: float64
- name: fan_speed_pct
dtype: float64
- name: mem_util_pct
dtype: float64
- name: qubic_tick_trace
dtype: float64
- name: qubic_tick_rate
dtype: float64
- name: qubic_epoch_progress
dtype: float64
- name: row_index
dtype: int64
splits:
- name: train
num_bytes: 84653192
num_examples: 813973
download_size: 6864325
dataset_size: 84653192
- config_name: mining
features:
- name: hashrate_mh
dtype: float64
- name: power_w
dtype: float64
- name: gpu_temp_c
dtype: float64
- name: reward_hint
dtype: float64
- name: qubic_tick_trace
dtype: float64
- name: qubic_tick_rate
dtype: float64
- name: qubic_epoch_progress
dtype: float64
- name: timestamp
dtype: string
- name: blockchain
dtype: string
- name: block_height
dtype: int64
- name: chain_epoch
dtype: int64
splits:
- name: train
num_bytes: 12670089
num_examples: 120322
download_size: 5763683
dataset_size: 12670089
- config_name: hft
features:
- name: timestamp
dtype: string
- name: step
dtype: int64
- name: action
dtype: string
- name: asset
dtype: string
- name: price_usd
dtype: float64
- name: quantity
dtype: float64
- name: trade_value_usdt
dtype: float64
- name: realized_pnl_usdt
dtype: float64
- name: balance_usdt
dtype: float64
- name: balance_dnx
dtype: float64
- name: balance_sol
dtype: float64
- name: balance_render
dtype: float64
- name: balance_asi
dtype: float64
- name: balance_near
dtype: float64
- name: balance_btc
dtype: float64
- name: balance_pepe
dtype: float64
- name: render_qty
dtype: float64
- name: asi_qty
dtype: float64
- name: near_qty
dtype: float64
- name: btc_qty
dtype: float64
- name: pepe_qty
dtype: float64
- name: cumulative_pnl
dtype: float64
- name: portfolio_value
dtype: float64
- name: ch2_mode
dtype: string
- name: reason
dtype: string
splits:
- name: train
num_bytes: 7591295
num_examples: 31573
download_size: 2604075
dataset_size: 7591295
- config_name: qubic_ticks
features:
- name: timestamp
dtype: timestamp[ms]
- name: tick
dtype: int64
- name: tick_rate
dtype: float64
- name: qubic_tick_trace
dtype: float64
- name: hashrate_mh_derived
dtype: float64
- name: power_w_derived
dtype: float64
- name: gpu_temp_c_derived
dtype: float64
- name: reward_hint_derived
dtype: float64
splits:
- name: train
num_bytes: 1755520
num_examples: 27430
download_size: 637843
dataset_size: 1755520
- config_name: gpu_telemetry_v3
features:
- name: row_index
dtype: int64
- name: ts_utc
dtype: int64
- name: ts_synthetic_offset_ms
dtype: int64
- name: schema_version
dtype: string
- name: vddcr_gfx_v
dtype: float64
- name: vram_temp_c
dtype: float64
- name: gpu_temp_c
dtype: float64
- name: power_w
dtype: float64
- name: gpu_clock_mhz
dtype: float64
- name: mem_clock_mhz
dtype: float64
- name: fan_speed_pct
dtype: float64
- name: mem_util_pct
dtype: float64
splits:
- name: full
num_bytes: 79158875
num_examples: 813973
download_size: 6090892
dataset_size: 79158875
- config_name: qubic_signals
features:
- name: row_index
dtype: int64
- name: ts_utc
dtype: int64
- name: ts_synthetic_offset_ms
dtype: int64
- name: schema_version
dtype: string
- name: qubic_tick_trace
dtype: float64
- name: qubic_tick_rate
dtype: float64
- name: qubic_epoch_progress
dtype: float64
splits:
- name: full
num_bytes: 46599955
num_examples: 813973
download_size: 3734815
dataset_size: 46599955
- config_name: state_telemetry
features:
- name: ts_utc
dtype: int64
- name: episode_id
dtype: string
- name: step_idx
dtype: int32
- name: schema_version
dtype: string
- name: window_ms
dtype: int32
- name: gpu_util_pct
dtype: float32
- name: mem_util_pct
dtype: float32
- name: fb_used_mib
dtype: float32
- name: fb_total_mib
dtype: float32
- name: power_w
dtype: float32
- name: power_limit_w
dtype: float32
- name: gpu_temp_c
dtype: float32
- name: vram_temp_c
dtype: float32
- name: sm_clock_mhz
dtype: float32
- name: mem_clock_mhz
dtype: float32
- name: fan_speed_pct
dtype: float32
- name: vddcr_gfx_v
dtype: float32
- name: pcie_replay_counter
dtype: int64
- name: ecc_sbe_vol
dtype: int64
- name: ecc_dbe_vol
dtype: int64
- name: throttle_reasons
dtype: int64
- name: thr_sw_power_cap
dtype: bool
- name: thr_hw_slowdown
dtype: bool
- name: thr_sw_thermal
dtype: bool
- name: thr_hw_thermal
dtype: bool
- name: thr_hw_power_brake
dtype: bool
- name: num_requests_running
dtype: int32
- name: num_requests_waiting
dtype: int32
- name: gpu_cache_usage_perc
dtype: float32
- name: ttft_p50_s
dtype: float32
- name: ttft_p99_s
dtype: float32
- name: itl_p50_s
dtype: float32
- name: tokens_per_s
dtype: float32
- name: prompt_tokens_total
dtype: int64
- name: generation_tokens_total
dtype: int64
- name: num_preemptions_total
dtype: int64
- name: e2e_latency_p99_s
dtype: float32
- name: request_timeouts
dtype: int64
- name: cpu_util_pct
dtype: float32
- name: cpu_temp_c
dtype: float32
- name: ram_used_pct
dtype: float32
- name: board_power_w
dtype: float32
- name: chassis_fan_rpm
dtype: int32
- name: fpga_spike_rate_hz
dtype: float32
- name: fpga_membrane_q88
dtype: int16
- name: fpga_clock_gated
dtype: bool
- name: fpga_temp_c
dtype: float32
- name: fpga_uart_frame_seq
dtype: int64
- name: source_host
dtype: string
- name: gpu_uuid
dtype: string
- name: regime
dtype: string
- name: synthetic
dtype: bool
splits:
- name: train
num_bytes: 136002048
num_examples: 569344
- name: validation
num_bytes: 28374528
num_examples: 118784
- name: test
num_bytes: 28104378
num_examples: 117653
download_size: 3857852
dataset_size: 192480954
- config_name: outcomes
features:
- name: episode_id
dtype: string
- name: step_idx
dtype: int32
- name: ts_utc
dtype: int64
- name: schema_version
dtype: string
- name: reward
dtype: float32
- name: d_gpu_temp_c
dtype: float32
- name: d_ttft_p99_s
dtype: float32
- name: d_tokens_per_s
dtype: float32
- name: d_kv_cache_usage
dtype: float32
- name: thermal_violation
dtype: bool
- name: oom
dtype: bool
- name: timeout_burst
dtype: bool
- name: sla_met
dtype: bool
- name: discount
dtype: float32
- name: is_terminal
dtype: bool
- name: is_first
dtype: bool
- name: is_last
dtype: bool
splits:
- name: train
num_bytes: 34748277
num_examples: 569344
- name: validation
num_bytes: 7249672
num_examples: 118784
- name: test
num_bytes: 7180671
num_examples: 117653
download_size: 1396646
dataset_size: 49178620
- config_name: encoding_params
features:
- name: feature_name
dtype: string
- name: encoder_type
dtype: string
- name: min
dtype: float64
- name: max
dtype: float64
- name: base_rate_hz
dtype: float32
- name: max_rate_hz
dtype: float32
- name: dt_seconds
dtype: float32
- name: q8_8_scale
dtype: int32
- name: threshold
dtype: float32
- name: schema_version
dtype: string
splits:
- name: train
num_bytes: 2587
num_examples: 36
download_size: 3510
dataset_size: 2587
language:
- en
tags:
- neuromorphic
- spiking-neural-networks
- snn
- liquid-state-machine
- leaky-integrate-and-fire
- stdp
- e-prop
- hardware-aware-ai
- fpga
- q8-fixed-point
- rust
- julia
- cuda
- gpu-telemetry
- blockchain
- cryptocurrency
- dynex
- qubic
- kaspa
- monero
- quai
- verus
- hft
- time-series
- bci
- med-tech
- neuromorphic-computing
- spike-train
- feature-extraction
- real-time-inference
- mining
- hashrate
- telemetry
- reinforcement-learning
- rlds
- action-proposal
- safety-filter
- vllm
- nvml
datasets:
- rmems/Spikenaut-SNN-Telemetry
library_name: spikenaut
pipeline_tag: reinforcement-learning
🧠 Spikenaut SNN Telemetry Dataset
"The threshold at which stimulus becomes perceptible"
Telemetry for the Spikenaut Supervisor control stack: v3 restructures this corpus from time-series forecasting into an action-proposal trajectory dataset — states, proposed actions, safety-filter verdicts, and outcomes — while every v2 config remains published, byte-identical and loadable.
The control hierarchy this dataset serves:
learned policy → action proposal → deterministic safety filter → execution → logged trajectory
The deterministic safety filter is always the final authority. Spikenaut (the learned SNN policy) only proposes. This dataset is structured so that training, evaluation, and audit all respect that boundary.
📊 Dataset Overview
v3 trajectory configs (Parquet, splits: train / validation / test)
| Config | Rows | Splits | Description |
|---|---|---|---|
state_telemetry |
805,781 | 569,344 / 118,784 / 117,653 | Aligned state windows in the full v3 supervisor schema (52 columns) |
outcomes |
805,781 | 569,344 / 118,784 / 117,653 | Horizon-H deltas, event flags, RLDS step flags |
gpu_telemetry_v3 |
813,973 | full (complete capture) |
v2 GPU sensors, cleanly separated from Qubic signals |
qubic_signals |
813,973 | full (complete capture) |
The qubic_* columns split out of the GPU stream |
encoding_params |
36 | train |
Per-feature spike-encoding sidecar (axon-encoder parameters) |
gpu_telemetry_v3 and qubic_signals are flat corpus derivations — they
deliberately cover all episodes (train, validation, test, and embargo), so
they publish under a single full split rather than a misleading train
label. Episode membership is row_index // 4096 against the block boundaries
in Episode & Split Design below. For leakage-safe training, use
state_telemetry.
Two further v3 tables ship as typed, empty Parquet schema artifacts and are deliberately not listed as loadable configs yet:
| Schema artifact | Path | Why empty |
|---|---|---|
action_proposals |
v3/action_proposals/ |
The deterministic teacher refuses to label states whose core signals (NVML throttle mask, ECC counters) were never collected — the v2 backfill therefore has zero labels, and a guessed label would be worse than none. |
safety_filter_log |
v3/safety_filter_log/ |
No safety filter ran during the v2 captures; there are no verdicts to publish. |
They are excluded from the viewer configs because datasets (≤ 5.0.x) cannot
batch a 0-row Parquet file (ArrowInvalid: BatchSize must be greater than 0).
Each gains a config entry with its first populated release; the schemas are
already frozen in the Parquet files and in
v3_build.py.
v2 configs (same records, still authoritative for the raw captures)
| Config | Records | Window | Description |
|---|---|---|---|
gpu_telemetry |
813,973 | — (no timestamps in source) | RTX 5080 sensors: power, temps, clocks, utilization |
mining |
120,322 | 2026-03-19 11:55 → 03-20 14:05 | Multi-coin node sync telemetry |
hft |
31,573 | 2026-03-11 18:22 → 03-12 02:40 | Ghost Money paper-trading log (simulated, not live capital) |
qubic_ticks |
27,430 | 2026-03-20 08:55 → 03-21 08:46 UTC | Qubic tick stream in SNN format |
These are disjoint capture windows, not one continuous run.
Serving format: the v2 configs load from verified Parquet conversions
under v2_parquet/ — a script-less Hub dataset gets exactly one builder,
inferred from the first config's files, so JSONL and Parquet configs cannot
share a card (see the 2026-08-16 changelog). The original JSONL files remain
in full_data/, byte-untouched and canonical; the conversions are produced
and fidelity-checked by the ETL (identical rows, columns, and nulls; the one
representation change is qubic_ticks.timestamp: timestamp[s] →
timestamp[ms], same values at finer unit).
from datasets import load_dataset
state = load_dataset("rmems/Spikenaut-SNN-Telemetry", "state_telemetry") # train/validation/test
outcome = load_dataset("rmems/Spikenaut-SNN-Telemetry", "outcomes")
enc = load_dataset("rmems/Spikenaut-SNN-Telemetry", "encoding_params", split="train")
v2 = load_dataset("rmems/Spikenaut-SNN-Telemetry", "mining", split="train")
All v3 trajectory tables join on (episode_id, step_idx); ts_utc is the
time key where it exists.
🛡️ Control Hierarchy & Safety
- The deterministic layer is final. A learned proposal is data into the
safety filter, never a command.
safety_filter_log.filter_verdictrecords what the filter did with each proposal:allow,modify,veto, orsubstitute, plus thefilter_rule_idthat fired. - Deployment modes (
safety_filter_log.mode):shadow(policy proposes, filter logs, humans/heuristics act),assist(filtered proposals surface as suggestions),active(filtered proposals execute). Promotion between modes is a human decision informed by this dataset. - Shadow KPI: filter-override rate — the share of proposals the filter did
not pass through unchanged (
verdict != "allow"). A policy is not a candidate forassistwhile its override rate is materially nonzero on the scenarios that matter. override/override_reasonrecord the rare, logged case of a human overriding the filter — the field exists so that such events are data, not folklore.
📐 Telemetry Contract
schema_version(column in every v3 config, currently3.0.0) is the semantic version of the telemetry contract: PATCH = docs/units clarifications, MINOR = additive nullable columns, MAJOR = anything breaking (rename, retype, resample).- Missing means
null— never0.0, never"". Columns whose collectors do not exist yet are present, typed, and null (schema first, backfill later). syntheticflags rows not produced by a real collector; the entire current backfill is measured data, so it isfalseeverywhere.regimeis null in the backfill: classifying idle/ramp/overload from values would be inference, not provenance.
Sampling rates and time
| Source | Cadence | Time columns |
|---|---|---|
v2 GPU capture (→ state_telemetry backfill) |
undocumented — the collector emitting these columns is not in rmems/Theseus-Quarry, and no interval is recorded anywhere |
ts_utc null, ts_synthetic_offset_ms null |
v2 mining |
irregular (~1 Hz bursts) | real timestamp strings (see v2 section) |
v2 hft, qubic_ticks |
event-driven | real timestamps |
| Future v3 collectors | must publish window_ms per row |
ts_utc int64, ns since Unix epoch |
No timestamp in this dataset is ever synthesized. ts_utc is nullable
int64 nanoseconds since epoch. ts_synthetic_offset_ms was designed as
row_index × documented sampling interval; because no sampling interval is
documented for the GPU capture, it ships null rather than encoding a guess.
(The dataset's own history shows why: 114,250 fabricated timestamps were once
published as a real collection window. Never again.) When the cadence is
confirmed from the collector source, populating the column is a MINOR bump.
Ordering within an episode is still exact via step_idx.
state_telemetry columns (52)
| Block | Columns | Units / notes | v2 backfill? |
|---|---|---|---|
| Keys | ts_utc (int64 ns), episode_id (str), step_idx (int32), schema_version (str), window_ms (int32) |
alignment window length | keys yes; ts_utc/window_ms null |
| GPU/NVML | gpu_util_pct, mem_util_pct, fb_used_mib, fb_total_mib, power_w, power_limit_w, gpu_temp_c, vram_temp_c, sm_clock_mhz, mem_clock_mhz, fan_speed_pct, vddcr_gfx_v (float32) |
%, MiB, W, °C, MHz, V | mem_util_pct, power_w, gpu_temp_c, vram_temp_c, sm_clock_mhz*, mem_clock_mhz, fan_speed_pct, vddcr_gfx_v |
| GPU counters | pcie_replay_counter, ecc_sbe_vol, ecc_dbe_vol (int64) |
monotone counters | null |
| Throttle | throttle_reasons (int64 bitmask) + decoded thr_sw_power_cap, thr_hw_slowdown, thr_sw_thermal, thr_hw_thermal, thr_hw_power_brake (bool) |
NVML nvmlClocksThrottleReasons bits 0x4/0x8/0x20/0x40/0x80 |
null |
| Inference server (vLLM metric names) | num_requests_running, num_requests_waiting (int32), gpu_cache_usage_perc (float32, 0–1), ttft_p50_s, ttft_p99_s, itl_p50_s, e2e_latency_p99_s (s), tokens_per_s, prompt_tokens_total, generation_tokens_total, num_preemptions_total, request_timeouts |
s, tokens/s, counts | null |
| CPU/board | cpu_util_pct, cpu_temp_c, ram_used_pct, board_power_w (float32), chassis_fan_rpm (int32) |
%, °C, W, RPM | null |
| FPGA | fpga_spike_rate_hz (float32), fpga_membrane_q88 (int16, Q8.8), fpga_clock_gated (bool), fpga_temp_c (float32), fpga_uart_frame_seq (int64) |
Hz, Q8.8, °C | null |
| Provenance | source_host, gpu_uuid, regime ∈ {idle, ramp, steady, overload, fault, synthetic}, synthetic (bool) |
synthetic=false; rest null |
* sm_clock_mhz is backfilled from v2 gpu_clock_mhz (the collector sampled
a single graphics clock; NVML SM and graphics clocks are reported separately on
paper but were one reading here). The redundant v2 clock_mhz duplicate was
verified equal and dropped.
Q8.8 convention
FPGA-side quantities use Q8.8 signed fixed point: value = int16 / 256,
range −128.0 … +127.99609375, 1 LSB = 1/256 ≈ 0.0039. fpga_membrane_q88
stores the raw int16; encoding_params.q8_8_scale = 256 records the scale so
any backend can reproduce the mapping. This matches the Limen-Neural hardware
contract (256-neuron / 1024-weight envelope, .mem export in Q8.8).
🎛️ Action Taxonomy
*_action / *_action_id columns use this frozen mapping (append-only, never
renumbered):
| id | action | id | action |
|---|---|---|---|
| 0 | no_op |
7 | shed_load |
| 1 | throttle_clocks |
8 | reroute_request |
| 2 | power_gate_fpga |
9 | migrate_workload |
| 3 | clock_gate_fpga |
10 | cap_power |
| 4 | reduce_batch_size |
11 | raise_fan |
| 5 | evict_kv_cache |
12 | pause_workload |
| 6 | downshift_precision |
13 | escalate_to_human |
Deterministic teacher rules (v1.0.0)
Implemented and unit-tested in
teacher_policy.py;
every label records the rule that produced it in
action_proposals.teacher_rule_id, with label_source = "teacher_rule" and
label_confidence = 1.0 (deterministic rules are certain by definition).
First match wins:
| Rule | Trigger | Teacher action |
|---|---|---|
TR-001-ECC-DBE |
ecc_dbe_vol > 0 |
escalate_to_human (+ paired migrate_workload in proposed_params.paired_action) |
TR-002-HW-POWER-BRAKE |
throttle bit 0x80 |
cap_power |
TR-003-HW-THERMAL |
throttle bit 0x40 |
throttle_clocks |
TR-004-HW-SLOWDOWN |
throttle bit 0x8 |
throttle_clocks |
TR-005-SW-THERMAL |
throttle bit 0x20 |
raise_fan |
TR-007-KV-PRESSURE-BATCH |
gpu_cache_usage_perc ≥ 0.95 sustained 6 windows |
reduce_batch_size |
TR-006-KV-PRESSURE-EVICT |
gpu_cache_usage_perc ≥ 0.95 sustained 3 windows |
evict_kv_cache |
TR-009-QUEUE-PREEMPT |
queue strictly growing 3 windows, throughput flat (±5%), preemptions rising | reroute_request |
TR-008-QUEUE-GROWTH |
queue strictly growing 3 windows, throughput flat (±5%) | shed_load |
TR-000-NOMINAL |
core signals present, nothing above fired | no_op |
Rules key off the NVML throttle bitmask, never hard-coded temperatures — a
GPU at 85 °C with no slowdown bit set is a GPU doing its job. Idle (0x1) and
sw_power_cap (0x4) bits describe normal operation and map to no_op.
If throttle_reasons or ecc_dbe_vol is null, the teacher emits no label
at all — which is why action_proposals is currently empty (see Migration).
⚡ Encoding
Policy: store raw values plus encoding parameters — never only pre-encoded
spike trains. Spikes are a lossy function of encoder settings; raw + params
lets axon-encoder (or any
backend) re-encode for a different simulator, timestep, or hardware target.
encoding_params is the sidecar (one row per state feature):
encoder_type∈ {rate, delta, latency, population, poisson, temporal, derivative} — the default assignment isratefor continuous magnitudes anddelta(threshold = 1 count) for monotone counters.min/max— the encoder input range, fitted on the train split only (leakage rule); null for features with no data yet.base_rate_hz/max_rate_hz— firing rates mapped to range endpoints (defaults 5 → 100 Hz), matchingRateEncoder::try_new(base_rate_hz, max_rate_hz, (min, max), dt_seconds).dt_seconds— the encoder's replay integration step (default 0.010). This is a prescriptive encoding choice, not a claim about collection cadence (which is undocumented for the GPU source). Batch encoding usesp = 1 − exp(−rate_hz · dt_seconds).q8_8_scale = 256— see Q8.8 convention above.
🧩 Episode & Split Design
- Episodes are fixed 4,096-step windows over the verified-contiguous
row_indexorder (gpu-000000…gpu-000198; the final episode is short: 2,965 steps). The capture has no wall clock, so bounded windows — not load-event segmentation — are the honest boundary choice for the backfill. - RLDS flags live in
outcomes:is_first/is_lastmark episode edges; episodes end by windowing, sois_last = trueis truncation andis_terminalstaysfalse;discount = 1.0. - Outcome horizon H = 64 steps.
d_gpu_temp_c[t] = gpu_temp_c[t+64] − gpu_temp_c[t], computed strictly within an episode; the last 64 steps of each episode are null. The other deltas (d_ttft_p99_s,d_tokens_per_s,d_kv_cache_usage) and the event flags await their collectors and are null.rewardis null: no reward function is defined yet, and publishing one implicitly through data would bypass review. - Splits are chronological blocks (≈70/15/15 by episode):
train =
gpu-000000…gpu-000138, validation =gpu-000140…gpu-000168, test =gpu-000170…gpu-000198. Episodesgpu-000139andgpu-000169(8,192 rows) are embargo gaps published in no split, so blocks are never temporally adjacent and no 64-step outcome window can cross a boundary. - Leakage rules: never random-shuffle across time; no episode spans two
splits; fit normalization/encoding statistics (including
encoding_paramsmin/max) on train only; anything fitted elsewhere is a bug.
🔍 v2 Schemas
Click to expand the v2 config documentation (unchanged from the v2 card)
gpu_telemetry — neuromorphic_data.jsonl
12 sensor columns + row_index. This source carries no timestamp — the
collector never emitted one; records are ordered but not time-located, and a
synthetic clock is deliberately not supplied. qubic_tick_trace,
qubic_tick_rate, qubic_epoch_progress ride along in this file for
continuity; v3 splits them into qubic_signals.
mining — node_sync_harvest.jsonl
Chain attribution comes from the source's timestamp field, which takes four forms:
| Rows | Source form | Result |
|---|---|---|
| 114,238 | 2026-03-19 11:55:13.132 |
real timestamp, blockchain: null |
| 5,001 | dynex:919876 |
blockchain: "dynex", block_height, timestamp: null |
| 1,083 | qubic:204:46075040 |
blockchain: "qubic", chain_epoch, block_height, timestamp: null |
| 12 | trailing placeholders | excluded (quarantined by the pipeline; see 2026-08-04 changelog) |
114,238 rows carry no chain label — that information does not exist in the
source. They are null, never "".
hft — ghost_market_log.jsonl
25 columns of paper-trading state. Actions: buy 14,603 / sell 14,580 /
observe 2,390. Simulated trading — not live capital.
qubic_ticks — qubic_ticks_snn.jsonl
The _derived columns are not measurements. They are a fixed function of
tick_rate, kept for continuity. The independent signals are tick_rate and
qubic_tick_trace.
Model artifacts — ⚠️ provenance unverified
full_data/snn_model.json, full_data/hybrid_training_results.json, and
models/mining_v2/*.mem are not reproducible from this repository and show
signatures consistent with untrained placeholders (identical weight vectors,
denormal weights, contradictory thresholds). They are under audit. Do not treat
them as trained parameters; the previously published 95.2% accuracy figure has
no reproducible basis.
📈 Provenance
rmems/Theseus-Quarry Rust collectors → raw JSONL
↓
rmems/spikenaut-telemetry-etl ingest → validate → clean → publish (full_data/ JSONL)
↓ └─ spikenaut-etl build-v3 ─────────────→ (v3/ + v2_parquet/)
rmems/Spikenaut-SNN-Telemetry this dataset
↓
rmems/Spikenaut-SNN model training
Every file here is generated. Do not hand-edit them; report data issues against the ETL repository. The v2 gates (no constant/all-null columns, distinct-row ratio, bounded drift, non-fabricated timestamps, exact schema match) still run; the v3 builder adds its own non-degeneracy asserts and refuses to write a broken tree.
🔀 Migration: v2 → v3
Everything v2 is preserved. Same files, same configs, same bytes; v3 is
purely additive under v3/. If you consume gpu_telemetry, mining, hft,
or qubic_ticks today, nothing changes for you.
| You used | Consider instead |
|---|---|
gpu_telemetry with the interleaved qubic_* columns |
gpu_telemetry_v3 + qubic_signals |
| ad-hoc train/test splitting of GPU rows | state_telemetry splits (chronological, embargoed) |
| hand-rolled spike encodings | raw values + encoding_params |
📝 Changelog
2026-08-16 — all configs Parquet-served; viewer restored
The 2026-08-15 release broke the Dataset Viewer for every v3 config
(JSON parse error: Invalid value. in row 0): a script-less Hub dataset
resolves one packaged builder from the first config's data files
(datasets/load.py, HubDatasetModuleFactoryWithoutScript) and applies it to
every config — so the JSON builder was fed Parquet bytes. Mixed JSONL/Parquet
configs cannot work on the Hub.
Since the v3 contract mandates Parquet, the four v2 configs now load from
Parquet conversions under v2_parquet/, generated and fidelity-verified by
spikenaut-etl build-v3 (ETL PR #9): identical row counts, rows, columns,
and nulls, with one representation change — qubic_ticks.timestamp is
timestamp[ms] instead of timestamp[s] (Parquet has no seconds resolution;
values unchanged). full_data/*.jsonl are byte-untouched and remain the
canonical cleaned exports. Same-day earlier fix: dataset_info dtypes were
serialized as repr strings (Value('float64')), which the viewer's
config-names step cannot parse; now generated with Features._to_yaml_list()
and round-trip-validated before every card commit.
2026-08-15 — v3.0.0: action-proposal trajectory restructure
Built by spikenaut-etl build-v3
(PR #7, merged as
983323b), teacher policy v1.0.0.
- New configs:
state_telemetry,outcomes,gpu_telemetry_v3,qubic_signals,encoding_params; schema artifactsaction_proposals,safety_filter_log(typed, empty — see Overview). Task categoryreinforcement-learningadded;time-series-forecastingretained for the v2 configs. Themetrics: accuracyfrontmatter key was removed — it referred to the disavowed 95.2% figure (see Model artifacts). - Zero teacher labels emitted over the v2 backfill — the historical GPU capture has no NVML throttle mask and no ECC counters, and the teacher does not guess. This is the intended, honest result; labels begin when collectors publish the core signals.
ts_utc/ts_synthetic_offset_msship null for the GPU-derived configs: no sampling interval is documented anywhere for that capture, and this dataset does not fabricate clocks (see 2026-08-03 entry for what happened last time someone did).- Step-0 verification notes, for the record:
mining,hft, andqubic_ticksdo carry real in-band timestamps (onlygpu_telemetryis time-blind);hfthas 25 columns;clock_mhzwas verified byte-equal togpu_clock_mhzacross all 813,973 rows before being dropped fromgpu_telemetry_v3;row_indexwas verified contiguous 0…813,972. - Decisions taken where the spec left room, recorded here: episodes are fixed
4,096-step windows (no wall clock to anchor load events); embargo = one full
episode per boundary (≥ H = 64);
regimeleft null in backfill rather than inferred;sm_clock_mhzbackfilled from v2gpu_clock_mhz; v2mining/hft/qubic_tickskeep their in-band timestamps and are not duplicated into v3 configs.
2026-08-04 — 12 placeholder rows removed from mining
Trailing rows appended out of order (~21 h backwards), the only rows carrying a
UTC offset, holding two distinct (power_w, gpu_temp_c) pairs. Quarantined;
the pipeline now fails the build on any unquarantined time reversal.
mining 120,334 → 120,322.
2026-08-03 — data rebuilt from recovered originals
neuromorphic_data.jsonl (813,973 rows of {"telemetry":{}}) and
node_sync_harvest.jsonl (every numeric 0.0) were rebuilt from recovered
originals. Cause: a Symbol/String key mismatch in the old Julia cleaning
script; every filter matched nothing, every lookup returned its default, and
nothing asserted the output was non-degenerate. The same script overwrote
114,250 real timestamps with a fabricated base + 10s × index sequence.
Also: chain attribution recovered (dynex 5,001 / qubic 1,083); qubic derived
columns renamed *_derived; samples regenerated as seeded random draws; 17
dead GPU columns and 13 dead mining columns dropped; model artifacts flagged
unverified. If you pulled a revision before 2026-08-03, re-download.
📜 Citation
@dataset{spikenaut_snn_telemetry,
author={Montoya Cardenas, Raul},
title={Spikenaut SNN Telemetry Dataset},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/datasets/rmems/Spikenaut-SNN-Telemetry}
}
⚖️ License
MIT OR Apache-2.0 — see LICENSE. Dual-licensed: use whichever fits your project.
🙏 Acknowledgments
- Kaspa, Monero, Qubic, Quai, Dynex, Verus communities for open-source node implementations
- E-prop authors (Bellec et al., 2020) and STDP pioneers (Bi & Poo, 1998)
- vLLM for the inference-server metric vocabulary mirrored in
state_telemetry
Built by Raul Montoya Cardenas — WGU AI Engineering